Selection from the list of topics

Estimates and standard error of the estimates

The results published in the NFI tables are estimates for variables in the Swiss forest (population parameters) whose true values are not known and must therefore be extrapolated (estimated) from the NFI sample data.

The extrapolations carry uncertainties. However, the accuracy of the extrapolations can be reliably estimated from the NFI sample, which is designed to be a random sample. To this end, all NFI tables include a second figure in addition to the estimate itself, namely the standard error of the estimate.

In most tables the percent standard error is reported (±%), but the absolute standard error (±) is also reported occasionally, especially for estimated percentages. The relationship between absolute and percent standard error is as follows:

percent standard error = absolute standard error / estimate × 100

absolute standard error = percent standard error × estimate / 100

Information on the forest road network is based on a full survey of all forest roads. In this case, there is no need to specify a standard error because there is no uncertainty caused by sampling.

Confidence interval of the estimate

With the estimate and the standard error of the estimate, the limits of the so-called confidence interval of the estimate can be calculated as:

lower limit = estimate – tQ × absolute standard error

upper limit = estimate + tQ × absolute standard error

If the simple standard error is used for the calculation (tQ = 1), then the 68% confidence interval is formed. In this case, the true value of the population parameter has a 68% probability of lying within this confidence interval of the estimate. If the double standard error is used (tQ = 2), then there is a 95% probability that the true value lies within this 95% confidence interval.

Significance of the estimate

The confidence interval can be used to statistically test whether the estimated population parameter is larger or smaller than a specific reference or target value, or whether two estimated population parameters truly differ (in the real population). For practical applications, proceed as follows: if a reference value lies outside the confidence interval, the estimated population value can be interpreted as being significantly different from it; if it lies within the confidence interval, the difference between the estimated population parameter and the reference value can be interpreted as random or not significant. When two population parameters are compared, they can be interpreted as differing significantly if their confidence intervals do not overlap.

Dealing with missing values

When calculating a results table, data are not always available for all combinations of characteristics^* of the classification variables and regional demarcation applied. In most cases, this indicates that the attribute estimated with the respective target variable does not occur or occurs only very rarely. Usually, the value 0 is used (imputed). However, as this value is not based on direct measurements, the associated standard error is represented with a full stop [.]. If the imputed value of 0 is referred to in the calculation, e.g. in the case of percentages or certain change estimates, no value can be entered. In this case, the estimated value and standard error are both represented with a full stop [.].

For example, no Arolla pines have been found and measured in the Swiss Plateau to date (growing stock of Arolla pines by production region). The values can be assumed to be missing because Arolla pine does not occur in the Swiss Plateau and therefore the growing stock there must be 0.

^* e.g. ‘Arolla pine’ characteristic of the classification variable ‘tree species’ and ‘Swiss plateau’ characteristic of the ‘regional demarcation’

Changes

There are two types of changes in the NFI:

The first type involves specific target variables relating to components of change, such as increment, fellings, mortality and losses. These target variables are only available for two consecutive inventory cycles, e.g. NFI3–NFI4. In the evaluation of components of change, the classification variable characteristic of the second inventory cycle is assigned to the first inventory cycle. These evaluations therefore do not take into account potential changes in a classification variable characteristic from the earlier to the later inventory (e.g. from private to public ownership).

With the second type of change, the difference in target variables, such as number of stems, growing stock or forest area, is used to assess the change between two inventory cycles. Target variables are generally used to represent states, such as that in NFI4, but can also indicate the net change between any two inventory cycles, e.g. NFI1–NFI4. In evaluations of change using these target variables, the change in a classification variable characteristic is considered in the analysis. Consequently, one can detect, for example, that the forest area without shrub forest increased between the two inventory cycles. This only has an effect on classification variables whose characteristic (class) can actually change, e.g. the affiliation with a forest area or the tree condition.

NFI5
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	43	350	19	155	26	771	18
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	329	29	663	20	330	30	1322	14
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	553	28	815	19	361	30	1729	14
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	543	27	920	21	209	32	1672	15
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	290	32	676	23	147	32	1113	16
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	205	37	293	31	14	100	512	23
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	167	38	241	39	0	.	408	28
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	75	232	54	40	100	341	41
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	71	48	100	38	100	143	51
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	88	72	0	.	128	58
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2517	20	4327	15	1294	22	8138	10
subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	3	100	0	.	0	.	55	61	240	36	16	100	199	25	117	41	57	61	657	19	894	17	318	21	2554	9
	21-30 cm	0	.	0	.	14	100	0	.	0	.	113	63	497	30	50	100	466	26	135	40	131	69	1164	17	1784	15	802	22	5156	8
	31-40 cm	0	.	0	.	0	100	0	.	0	.	194	52	736	31	10	100	840	24	255	42	473	51	1487	18	2913	13	984	20	7891	8
	41-50 cm	0	.	0	.	14	100	0	.	0	.	343	51	784	29	0	0	924	25	218	48	438	45	2007	19	3385	13	1065	21	9178	8
	51-60 cm	0	.	0	.	33	100	0	.	0	.	166	52	339	36	34	100	572	31	131	54	213	46	1884	17	2785	13	1169	20	7325	8
	61-70 cm	0	.	0	.	12	100	0	.	0	.	71	77	310	39	14	100	365	35	215	50	82	74	1409	21	1852	19	486	25	4817	11
	71-80 cm	0	.	0	.	0	.	0	.	0	.	69	71	86	60	34	100	440	32	45	100	138	60	797	35	782	25	318	32	2709	15
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	74	136	51	418	41	485	42	339	43	1444	22
	91-100 cm	0	.	0	.	0	.	0	.	0	.	57	100	0	.	0	.	0	.	0	.	38	100	143	58	170	58	0	.	408	36
	>100 cm	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	38	100	0	.	237	58	0	.	311	47
	total	0	.	0	.	77	100	0	.	0	.	1103	39	2991	24	157	71	3807	22	1183	36	1745	35	9965	13	15285	10	5480	15	41793	6
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	50	78	0	.	12	78	0	.	0	.	329	31	226	25	109	45	488	21	126	35	100	36	1365	13	1700	12	566	17	5070	6
	21-30 cm	13	100	0	.	33	100	0	.	0	.	531	35	617	26	228	36	642	21	518	28	137	39	2871	14	3119	12	1160	15	9870	7
	31-40 cm	36	60	0	.	53	82	0	.	0	.	852	26	748	22	328	37	1200	19	662	30	139	47	3180	14	3432	12	2058	17	12689	6
	41-50 cm	1	100	0	.	42	72	0	.	0	.	1015	24	766	25	502	36	1599	24	777	40	152	62	2980	13	4652	11	2213	14	14698	6
	51-60 cm	30	73	0	.	51	74	0	.	0	.	889	26	823	27	424	37	1758	21	538	33	76	51	2809	15	4146	13	2117	16	13659	7
	61-70 cm	77	100	0	.	24	100	0	.	0	.	735	37	129	47	296	48	1237	23	226	48	238	56	1372	22	2351	17	1367	19	8051	9
	71-80 cm	54	100	0	.	28	100	0	.	0	.	251	48	264	39	37	100	760	29	155	62	299	62	1047	25	1171	21	1129	27	5195	11
	81-90 cm	122	100	0	.	0	.	0	.	0	.	87	100	105	100	0	.	219	46	0	.	78	71	312	40	575	31	456	47	1953	19
	91-100 cm	71	100	0	.	0	.	0	.	0	.	123	72	0	.	0	.	167	58	0	.	172	58	188	50	226	51	206	58	1153	23
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	49	100	0	.	51	100	64	100	123	71	203	73	491	40
	total	454	57	0	.	242	61	0	.	0	.	4811	21	3679	21	1925	29	8116	16	3003	27	1442	41	16188	10	21496	9	11473	11	72829	4
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	2923	7	221	28	242	28	854	24	647	19	599	22	1960	11	1069	14	614	20	436	23	533	23	293	25	286	24	1401	13	12079	4
	21-30 cm	6037	7	645	24	532	31	1189	23	1459	16	1841	18	3784	10	3414	12	1140	17	838	23	960	25	583	25	748	24	2638	14	25808	4
	31-40 cm	9206	7	881	22	904	27	1423	20	2083	16	2517	16	5080	10	5817	10	1548	16	1071	23	1153	19	721	27	908	23	2447	21	35757	4
	41-50 cm	10707	6	1101	23	1348	24	1824	20	3117	14	2391	16	6123	10	6370	10	2079	18	1244	25	1869	17	680	28	1166	22	2068	19	42087	3
	51-60 cm	8490	7	1157	28	1449	24	1975	18	3203	16	2705	15	6661	10	6866	10	1473	20	1108	23	1905	18	539	38	1063	23	1540	28	40135	4
	61-70 cm	4537	12	376	38	850	24	1334	24	1767	21	1450	22	4201	13	4329	14	1094	25	694	32	853	28	306	36	944	22	831	22	23566	5
	71-80 cm	2249	15	245	55	452	38	601	34	1169	26	528	36	2070	17	2282	19	754	28	960	33	603	35	90	58	513	36	410	33	12925	7
	81-90 cm	625	32	0	.	366	45	441	40	351	47	233	51	1015	26	993	26	122	71	122	71	158	61	0	0	146	58	303	40	4875	11
	91-100 cm	269	50	0	.	0	.	67	100	280	53	0	.	109	71	113	71	129	71	68	100	178	62	21	100	0	.	212	58	1446	22
	>100 cm	255	59	0	.	0	.	0	.	0	.	0	.	263	61	179	58	0	.	0	.	0	.	0	.	0	.	111	100	808	33
	total	45297	4	4625	19	6144	19	9709	15	14075	12	12264	12	31265	7	31432	8	8952	14	6540	19	8213	15	3234	23	5775	17	11961	14	199485	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	573	21	789	17	821	23	1458	14	1639	12	11	100	301	44	125	39	172	37	116	74	38	100	89	54	78	39	0	.	6211	6
	21-30 cm	1174	18	1131	15	1292	19	2268	14	3242	11	14	100	529	28	384	45	151	53	224	50	39	87	90	64	160	42	0	.	10699	6
	31-40 cm	1633	16	1711	17	1619	18	3082	13	3847	11	33	100	597	30	506	31	241	48	289	51	14	72	129	58	123	49	0	.	13825	5
	41-50 cm	2053	15	2182	16	1879	18	3563	12	4267	11	20	100	784	34	462	37	313	38	308	45	50	76	26	72	160	43	0	.	16066	5
	51-60 cm	1675	19	1441	18	1626	19	4395	12	4424	10	0	.	625	31	301	40	355	43	159	58	93	100	84	71	198	46	0	.	15376	6
	61-70 cm	1002	26	1128	22	793	24	2722	16	3013	13	0	.	219	47	125	59	211	73	142	71	0	.	16	100	24	100	0	.	9395	7
	71-80 cm	229	41	622	30	244	39	1365	21	1608	19	43	100	346	43	45	100	38	100	38	100	0	.	18	100	33	100	0	.	4629	11
	81-90 cm	101	71	126	71	110	73	205	50	1147	25	0	.	0	.	115	72	0	.	81	71	0	.	0	.	0	.	0	.	1885	19
	91-100 cm	0	.	0	.	55	100	278	53	271	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	657	31
	>100 cm	112	100	0	.	134	71	150	71	287	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	683	41
	total	8551	13	9130	12	8573	14	19486	9	23745	7	122	100	3401	23	2063	31	1482	35	1356	41	235	72	452	43	829	37	0	.	79426	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	3	100	0	.	54	53	0	.	20	72	0	.	0	.	0	.	0	.	0	.	0	.	238	27	3	100	1839	11	2156	10
	21-30 cm	48	78	0	.	129	61	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	328	28	11	100	3101	11	3652	10
	31-40 cm	62	71	0	.	144	60	0	.	29	78	0	.	0	.	0	.	0	.	0	.	0	.	271	31	9	100	2282	11	2796	10
	41-50 cm	53	71	0	.	95	64	0	.	48	73	0	.	0	.	0	.	0	.	0	.	0	.	170	38	27	100	1788	13	2181	12
	51-60 cm	0	.	0	.	52	100	0	.	38	71	0	.	0	.	0	.	0	.	0	.	0	.	155	71	30	100	953	20	1229	18
	61-70 cm	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	493	24	561	23
	71-80 cm	0	.	0	.	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	255	32	301	31
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	411	55	411	55
	total	165	71	0	.	556	49	0	.	169	63	0	.	0	.	0	.	0	.	0	.	0	.	1192	26	113	100	11420	8	13615	7
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	3547	7	1010	14	1132	18	2313	12	2307	10	994	17	2727	10	1318	12	1473	12	795	19	729	19	2907	9	3312	8	4278	6	28841	3
	21-30 cm	7272	6	1776	13	2000	15	3457	12	4736	9	2499	15	5427	8	4078	11	2399	11	1715	16	1267	21	5365	9	6485	8	8031	6	56508	2
	31-40 cm	10937	6	2592	13	2720	14	4504	10	5958	8	3596	13	7161	8	6661	9	3828	11	2277	16	1780	18	6342	9	8200	7	8131	8	74688	2
	41-50 cm	12813	5	3282	13	3378	14	5387	10	7431	8	3769	12	8457	8	7334	9	4916	12	2547	18	2509	15	6405	9	10311	7	7343	7	85882	2
	51-60 cm	10195	6	2597	15	3211	15	6370	10	7665	8	3759	13	8447	8	7625	9	4157	12	1936	17	2287	16	5762	10	8898	8	5926	9	78837	2
	61-70 cm	5615	10	1504	18	1716	17	4056	13	4780	11	2256	19	4858	12	4765	13	2907	15	1277	22	1173	24	3307	13	5496	10	3191	10	46901	3
	71-80 cm	2531	14	867	27	770	26	1966	18	2776	15	892	26	2766	14	2397	18	1992	17	1198	28	1040	28	2118	18	2741	14	2112	16	26166	5
	81-90 cm	848	29	126	71	476	39	646	31	1497	22	320	46	1120	25	1108	24	341	39	269	43	373	35	798	27	1438	21	1291	23	10652	8
	91-100 cm	340	45	0	.	55	100	345	47	551	35	180	58	109	71	113	71	296	45	68	100	388	40	439	31	495	34	601	32	3980	12
	>100 cm	367	51	0	.	134	71	150	71	287	75	36	100	263	61	179	58	49	100	0	.	90	71	103	73	448	39	725	40	2830	18
	total	54467	4	13755	10	15592	11	29195	7	37989	5	18300	10	41336	6	35578	7	22357	9	12082	13	11634	13	33548	6	47824	5	41627	4	415285	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1321335. Birmensdorf, Swiss Federal Research Institute WSL

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